Zobrazeno 1 - 10
of 268
pro vyhledávání: '"Shin, Jiho"'
Retrieval Augmented Generation (RAG) has shown notable advancements in software engineering tasks. Despite its potential, RAG's application in unit test generation remains under-explored. To bridge this gap, we take the initiative to investigate the
Externí odkaz:
http://arxiv.org/abs/2409.12682
In this work, we revisit existing oracle generation studies plus ChatGPT to empirically investigate the current standing of their performance in both NLG-based and test adequacy metrics. Specifically, we train and run four state-of-the-art test oracl
Externí odkaz:
http://arxiv.org/abs/2310.07856
In this paper, we investigate the effectiveness of state-of-the-art LLM, i.e., GPT-4, with three different prompting engineering techniques (i.e., basic prompting, in-context learning, and task-specific prompting) against 18 fine-tuned LLMs on three
Externí odkaz:
http://arxiv.org/abs/2310.10508
Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code models to generate unit tests with the help of Domain Adaptation (DA) at
Externí odkaz:
http://arxiv.org/abs/2308.08033
Automatic detection of software bugs is a critical task in software security. Many static tools that can help detect bugs have been proposed. While these static bug detectors are mainly evaluated on general software projects call into question their
Externí odkaz:
http://arxiv.org/abs/2307.04080
Publikováno v:
ACM Transactions on Software Engineering and Methodology 33-2 (2023) 1-24
Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especially for develop
Externí odkaz:
http://arxiv.org/abs/2305.09082
The application of machine learning (ML) libraries has been tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerabilities of such libraries result in irreparable consequences. However,
Externí odkaz:
http://arxiv.org/abs/2203.06502
Explaining the prediction results of software defect prediction models is a challenging while practical task, which can provide useful information for developers to understand and fix the predicted bugs. To address this issue, recently, Jiarpakdee et
Externí odkaz:
http://arxiv.org/abs/2111.10901
Autor:
Tran, Xuan Tin, Mun, Dae Hun, Shin, Jiho, Kang, Na Young, Park, Dae Sung, Park, Yong-Ki, Choi, Jungkyu, Kim, Do Kyoung
Publikováno v:
In Fuel 1 April 2024 361
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